Products and methods for identifying rock samples

ABSTRACT

Products and methods for identifying rock samples based on an average color value for each rock sample.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/158,110 filed on Jun. 10, 2011, which claims the priority of U.S.Provisional Patent Application No. 61/401,934, filed on Aug. 23, 2010,and which are incorporated herein by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

Not applicable.

FIELD OF THE INVENTION

The present invention relates to products and methods for identifyingrock samples. More particularly, the present invention relates toidentifying rock samples based on an average color value for each rocksample.

BACKGROUND OF THE INVENTION

The crust of the Earth is composed of a great variety of igneous,metamorphic, and sedimentary rocks that generally sit in numerousdistinctive layers. In order to understand this complex structurechanges must be identified that distinguish one rock or mineral fromanother and to correlate them across wide lateral separation. Samples oflayered rock are easily collected from vertical outcrops, eitherman-made (e.g., road cuts) or natural (e.g., mountainsides or valleys).When outcrops are not available or when the rocks are very deep, wellsand cores provide access to samples of the rock not visible at thesurface. Whether understanding the substrate for construction,environmental studies, water resources, mining, or oil and gasextraction, it is critical to recognize and understand the regional andlocal geology.

Numerous tools and techniques exist that provide information about rocksand minerals that might be encountered, ranging from direct chemicalanalysis to remote sensing of a multitude of physical rock parameters.Analyses of rock samples can be performed in an onsite or distantlaboratory, or measurements can be taken remotely with tools loweredinto holes or wells drilled into the rock or sediment. Some techniquesare quite simple, and others are very expensive and complicated. Forexample, some tools and techniques measure different parameters, measurethem using different methods or from different locations, analyze themeasured data in different ways, and present the results of analysis ina variety of formats. Nevertheless, all of these tools and techniqueswork together to provide various properties and/or attributes ofinformation that a trained person can use to identify, understand, andcorrelate specific rocks and minerals.

When describing a rock or mineral, not all of the information that ageologist might use is easily quantifiable. Geologic descriptions arecommonly full of qualitative terminology and assessments. A geologistmight use words such as “sandy,” “shaley,” “greenish,” “gray,” or“translucent” that may describe the grain size, texture, color, and soforth. The geologist might further qualify such descriptions withvarious modifiers—such as “light” or “dark,” to better describe thespecific way a rock sample appears to an observer. Whereas the human eyeis good at seeing fine details and discriminating subtle distinctions oftexture and color, the human brain is not good at converting these finedistinctions into language that can be easily and clearly understood byanother person with the same level of detail as the observer's eye. Inaddition, the brain cannot retain an image with enough detail tounequivocally determine if one sample is identical to another samplepreviously observed. Even the use of color charts similar to those usedto match paint samples is limited in precision and repeatability.Geologists therefore, are not always able to easily and/or accuratelyquantify information observed during the study of rocks and mineralogy.

SUMMARY OF THE INVENTION

The present invention overcomes one or more deficiencies in the priorart by providing products and methods for identifying rock samples basedon an average color value for each rock sample.

In one embodiment, the present invention includes a method foridentifying a rock sample, comprising: i) acquiring data from the rocksample; and ii) determining an average color value for the rock samplefrom the data.

In another embodiment, the present invention includes a non-transitoryprogram carrier device tangibly carrying a data structure, the datastructure comprising a first data field comprising a well log, the welllog comprising a color value field and a depth field, wherein the colorvalue field comprises an average color profile and the average colorprofile represents an averaged image of average color values for eachrock sample.

Additional aspects, advantages and embodiments of the invention willbecome apparent to those skilled in the art from the followingdescription of the various embodiments and related drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described below with references to theaccompanying drawings in which like elements are referenced with likenumerals and which:

FIG. 1 is a flow diagram illustrating one embodiment of a method forimplementing the present invention.

FIG. 2A illustrates an original image (photograph) of cuttings from arock sample (left) compared to an “averaged” image and variousquantifications representing average color values for the rock sample(right).

FIG. 2B illustrates an original image (photograph) of a liquid slurrycontaining cuttings from a rock sample (left) compared to an “averaged”image and various quantifications representing average color values forthe rock sample (right).

FIG. 2C illustrates an original image (photograph) of a slide containinga liquid slurry with cuttings from a rock sample (left) compared to an“averaged” image and various quantifications representing average colorvalues for the rock sample (right).

FIG. 3 is an exemplary well log illustrating the results of step 108 inFIG. 1.

FIG. 4 is another exemplary well log illustrating the results of step108 in FIG. 1.

FIG. 5 is a comparison of exemplary well logs illustrating step 110 inFIG. 1.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The subject matter of the present invention is described withspecificity, however, the description itself is not intended to limitthe scope of the invention. The subject matter thus, might also beembodied in other ways, to include different steps or combinations ofsteps similar to the ones described herein, in conjunction with othertechnologies. Moreover, although the term “step” may be used herein todescribe different elements of methods employed, the term should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless otherwise expressly limited by thedescription to a particular order. While the following descriptionrefers to the oil and gas industry, the systems and methods of thepresent invention are not limited thereto and may also be applied toother industries to achieve similar results.

In both ancient (cable-tool) and modern (rotary) drilling, rock isusually pulverized and subsequently removed from a hole or well bore.The exception is coring, a process of removing an intact cylinder ofrock or sediment for preservation and later study. Nevertheless, thevast majority of rock available for study is in the form of “cuttings,”the name most commonly used for the rock chips removed from a well beingdrilled. These cuttings were once removed with buckets or balers fromholes and wells drilled using chiseling or percussion action to breakand penetrate the rock at the bottom of the hole. Today, fluid or airforced through the annulus of a length of hollow drill pipe carry thecuttings, ground or broken away by the teeth of a rotary drill bit onthe bottom end of the drill pipe, up to the surface through the annulusof the well between the wall of the well and the drill pipe. Thesecuttings are collected and sampled. The samples are bagged and labeled,making note of the depth from which a particular sample is associated.Samples are later analyzed in a variety of ways to look at mineralogywith X-ray diffraction and visual lithological description, at elementalabundance using X-ray fluorescence, and at the types and relativeabundance of tiny microscopic fossils and pollens that are present inthe sediment sampled.

Although the following method of analysis can be applied to core rocksamples as well as to rock samples in the form of cuttings, thefollowing description refers to cuttings since they are predominantsource of material to be analyzed.

Referring now to FIG. 1, a flow diagram illustrates one embodiment of amethod 100 for implementing the present invention.

In step 102, the rock sample (cuttings) is prepared using techniqueswell known in the art. The cuttings typically arrive in bags labeledwith the well name and depths with which they are associated. Each bagis opened in turn, and a portion of the cuttings within that bag areremoved and washed to remove drilling mud and additives which werecirculated through the well bore during rotary drilling. The cuttings,rock chips broken away from the drilled rock formation, are sieved andretained as the drilling mud is washed away. The washing processinvolves differential application of a combination of water, detergents,heat (ovens), and mechanical separation. The precise order andcombination of these depends upon a variety of factors, including thetype of rock, its cementation and hardness, the type of drilling mudused, and the types of analyses planned for the rock sample. Inaddition, certain drilling additives may be particulate in nature andmay not be easily separated from the cuttings during the washingprocess. This material can be manually separated and removed from theactual cuttings by a trained technician. Before analyses or beforemanual separation, the cuttings are dried, either in ovens or byevaporation, using fans at ambient temperatures.

Prior to the acquisition of data based on the cuttings, a portion ofeach sample may be placed into a small container (e.g. 90 m. paper cup)that is open at the top. Each sample is taken from a particular depth ofthe well being studied and is ready for the acquisition of data in step104.

Alternatively, the cuttings may be prepared in the form of a liquidslurry before the acquisition of data in step 104. A sample of thecuttings is placed into a grinder or mortar with approximately 20-ml ofdistilled or DI (de-ionized) water. Care must be taken not to over-grindthe sample. Hand grinding with mortar and pestle provides the greatestlevel of control. When the solid material is sufficiently ground, theparticles will remain suspended in the liquid, creating a slurry. Eachslurry may be placed into a 30-ml plastic cup.

It may also be more economical to combine the liquid slurry sample withother common techniques used to analyze and correlate cuttings and corematerial. A common form of study is the isolation, determination, andquantification of the microscopic fossils found in cuttings fromsedimentary rock formations. An important group of these fossils hasbeen classified by the obviously descriptive name “nannofossils” due tothe tiny size (generally less than 30 microns) of them. Anotheralternative technique for preparing the rock sample therefore, mayinclude creating slides with the liquid slurry that can be used by apaleontologist (biostratigrapher) and/or for data acquisition in step104. Although any size may work, common laboratory glass slidesmeasuring 75×25×1 mm are preferred. The glass slide is warmed toapproximately 70 degrees Celsius. The liquid slurry is vigorouslystirred with a clean glass rod for a few seconds. After standingundisturbed for about 45 seconds, a pipette or eyedropper is used toremove a portion of the liquid fraction containing the suspended solidcuttings. Several drops of this solution are placed on the warm glassslide and then smeared evenly across the surface of the slide. When thesolution has dried, the slide is ready for step 104.

In step 104, data is acquired from the rock sample prepared in step 102using a light source and photographic equipment or a spectrophotometer.The data is acquired by transmitting light at a rock sample andcapturing transmitted, refracted and/or reflected light withphotographic equipment or a spectrophotometer. The acquired data mayalso be referred to as spectral data, which may include data within andoutside the visible spectrum. Rock samples in the form of cuttings,slurries, slides, etc. are organized and laid out in a regular pattern(preferably about 1 cm apart) on a flat surface of uniform color(preferably white). Although use of a spectrophotometer can capture datawithin and outside the visible spectrum, other photographic equipmentmay be used. In either case, the photographic equipment (e.g. digitalcamera) or a spectrophotometer is suspended above the group of samples,and one or more photographs are taken. In order to reduce any externalvariable factors, the same camera should be used with identical settingsat a fixed distance (e.g. 80 cm.) from the flat surface to the focalpoint of the camera each time. The light source is also an importantfactor. It is best to use full spectrum light sources or bulbs, whichmay include, for example, a full spectrum fluorescent light source.Shadows can also be a problem. Therefore, using multiple sources anddevices to reflect and “soften” the light give better results. At leasttwo light sources may be used, which are physically separated from eachother and reflected from special umbrellas used by professionalphotographers, to provide softer light with less glare and fewershadows. These should be the only lights used during the acquisition ofdata (taking photographs). The room lights are turned off so that noextraneous light is reflected from the rock samples. If slurries arebeing analyzed, they should be stirred prior to data acquisition.

In step 106, an average color value is determined for each sample usingthe data acquired in step 104 and well known graphics programs such as,for example, Paint.NET®, which is publicly available freeware. Theuncompressed raw data acquired in step 104 should be used. Because theimages of multiple samples may be captured in a single photograph instep 104, each sample image is individually isolated and separatelyanalyzed to determine the average color values.

Because graphics programs may alter values or compress files whenattempting to save an image, a numerically-based color model ispreferred. A color model is an abstract mathematical model describingthe way colors can be represented as tuples (ordered lists of numbersused to describe other mathematical objects), typically as three or fourvalues or color components. Commonly used color models are RGB (Red,Green, Blue), CMYK (Cyan, Magenta, Yellow, blacK), and HSV (Hue,Saturation, Value). Any color model may be used, however, the RGB modelis illustrated in the following examples for its simplicity andfamiliarity. The RGB color model is an additive color model in whichred, green, and blue light are added together in various ways toreproduce a broad array of colors. The main purpose of the RGB colormodel is for the sensing, representation, and display of images inelectronic systems, such as televisions, computers, and digitalphotography. The color is expressed as an RGB triplet, and eachcomponent of the RGB triplet can vary from zero to a defined maximumvalue. If all of the components are at zero, the result is black. If allof the components are at a maximum value, the result is the brightestrepresentable white.

The color values may be quantified and represented in several differentways, for example:

-   -   From 0 to 1, with any fractional value in between. This        representation is used in theoretical analyses, and in systems        that use floating-point representations.    -   Each color component value can also be written as a percentage,        from 0% to 100%.    -   In computing, the color component values are often stored as        integer numbers in the range 0 to 255, the range that a single        8-bit byte can offer (by encoding 256 distinct values).    -   High-end digital image equipment can deal with the integer range        0 to 65,535 for each primary color, by employing 16-bit words        instead of 8-bit bytes.

Full intensity of the color component for red may therefore, be writtenin the different RGB notations illustrated in Table 1:

TABLE 1 Representation RGB triplet Numerical (1.0, 0.0, 0.0) Percentage(100%, 0%, 0%) Digital 8-bit per (255, 000, 000) Digital 16-bit per(65535, 0, 0)

In order to determine the average color value for a sample, the R-value,G-value, or B-value for each pixel is counted and the total R-values,G-values and B-values or divided by the total number of pixels.Preferably, the average color values for the sample are determined bycounting the R-value, G-value and B-value for each pixel and dividingthe total R-values, G-values and B-values by the total number of pixels.Each sample therefore, may have its own triplet of average RGB colorvalues as illustrated in FIGS. 2A, 2B and 2C. In addition, ratios of thecolor value components often prove to be useful when uniquelyidentifying and correlating rock samples from different sources.Capturing the data for each sample at a higher resolution translatesinto a greater number of pixels and more accurate average RGB colorvalues.

The individual color components of the average RGB color values can berepresented in other formats through mathematical operations andtransformations. For example, simple ratios of the individual (i.e.,R/G) and multiple (i.e., R/(R+G+B)) average color components createadditional representations of the average color values. Average colorvalues from one color system (e.g., RGB) can also be converted to othercolor systems (e.g, CMYK or HSV) through mathematical transformations.It is also possible to transform the average RGB color values into i) aformat easily recognized by computing applications for graphical display(e.g., hexadecimal format); and ii) an “averaged” image of the averageRGB color values for the sample. In FIGS. 2A, 2B and 2C, for example,the original image is on the left and an “averaged” image is on theright. The uniform color of each “averaged” image is associated with aunique hexadecimal value that was created from the average RGB colorvalues.

In step 108, a well log may be created based on the average color valuesand depth of each sample. The well log may be created in an Excelspreadsheet or other format using techniques well known in the artand/or conventional applications such as, for example, Oilfield DataManager™, which is licensed by Senergy. Because the depth from which asample was taken is a major factor in ordering the samples andcorrelating a group of samples from one well to a group taken fromanother well, it is convenient to place the data from step 106 in a welllog format.

In FIG. 3, an exemplary well log (well 1) illustrates the results ofstep 108. In FIG. 4, another exemplary well log (well 2) illustrates theresults of step 108. In each exemplary well log, the data may becompiled with the sample depth laid out vertically and with horizontalscaling. The columns displayed in FIG. 3 and FIG. 4 can vary fromnumerical traces of color values to average color profiles based on theaverage color value(s) for each sample. Adding numerical traces ofratios can isolate and enhance relative differences between color valuecomponents for identification of color variation. Displaying theaveraged image of the average color values for each sample in a verticalmanner creates a color profile in the well log that intuitivelyrepresents what the colors of the rock strata might look like whenlooking down the wellbore. Any number of samples can be plotted in eachwell log, with the proximity of intervals limited only by the density ofthe sample cuttings or core. Changes in the average color value(s) withdepth can be related to geologic variations within the sample.

In step 110, each well log created in step 108 may be correlated withanother well log created in step 108 and/or created from another source.Referring now to FIG. 5, a comparison of exemplary well logs (well 1 andwell 2) illustrates step 110. The comparison in FIG. 5 demonstrates howpatterns of color in one well log correlate to the patterns of anotherwell log. These correlations provide the geologist with key insightsinto the structure and stratigraphy of the area being studied.

The invention claimed is:
 1. A method for identifying a rock sample,comprising: acquiring data from the rock sample; determining an averagecolor value for the rock sample from the data; repeating the steps ofacquiring data and determining the average color value for each rocksample from a well; and creating a well log based on average colorvalues and a depth for each rock sample from the well.
 2. The method ofclaim 1, wherein the data is acquired by a digital camera or aspectrophotometer using a light source.
 3. The method of claim 2,wherein the data represents spectral data comprising light transmitted,reflected or refracted by the rock sample.
 4. The method of claim 3,wherein the data acquired by the camera or spectrophotometer isrepresented by a plurality of pixels in a photograph.
 5. The method ofclaim 4, wherein the average color value for the rock sample isdetermined by using a color model.
 6. The method of claim 5, wherein thecolor model includes an R-value, a G-value and a B-value for each pixel.7. The method of claim 6, wherein the average color value for the rocksample is determined by: counting the R-value, G-value and B-value foreach pixel; determining a total for all R-values, G-values and B-values;and dividing the total R-values, G-values and B-values by a total numberof pixels represented by the plurality of pixels.
 8. The method of claim1, wherein the rock sample is identified by the average color value. 9.The method of claim 1, further comprising: repeating the steps in claim1 for each rock sample from another well; and creating another well logbased on average color values and a depth for each rock sample from theanother well.
 10. The method of claim 9, further comprising correlatingthe well log with the another well log.
 11. The method of claim 10,wherein correlating the well log with the another well log includescomparing the average color values and the depth for each rock sample inthe well log with the average color values and the depth for each rocksample in the another well log.
 12. The method of claim 1, wherein thedata is acquired by a digital camera or a spectrophotometer using alight source and another light source.
 13. The method of claim 12,wherein the light source is a full spectrum fluorescent light source andthe another light source is an ultra-violet light source.
 14. The methodof claim 13, further comprising: repeating the steps in claim 1 for eachrock sample from a well; creating a well log comprising average colorvalues and a depth for each rock sample from the well, the average colorvalue determined for each respective rock sample from the data; andcomparing average color values from data acquired using the fullspectrum fluorescent light source and average color values from dataacquired using the ultra-violet light source.
 15. The method of claim14, further comprising: repeating the steps in claim 1 for each rocksample from another well; creating another well log comprising averagecolor values and a depth for each rock sample from the another well, theaverage color value determined for each respective rock sample from thedata; and comparing average color values from data acquired using thefull spectrum fluorescent light source and average color values fromdata acquired using the ultra-violet light source.
 16. The method ofclaim 15, further comprising correlating the well log with the anotherwell log.
 17. The method of claim 16, wherein correlating the well logwith the another well log includes comparing the average color valuesand the depth for each rock sample in the well log with the averagecolor values and the depth for each rock sample in the another well log.18. The method of claim 1, further comprising representing an averagedimage of average color values for each rock sample using an averagecolor profile and a non-transitory program carrier device tangiblycarrying a data structure, the data structure comprising a first datafield comprising the well log, the well log comprising a color valuefield and a depth field, wherein the color value field comprises theaverage color profile.
 19. The method of claim 18, wherein the averagedimage represents the rock sample from the well at the depth representedin the depth field.
 20. The method of claim 18, wherein the color valuefield comprises an average Red-value, an average Green-value or anaverage Blue-value.
 21. The method of claim 18, wherein the color valuefield comprises a ratio of color components.
 22. The method of claim 21,wherein the ratio of color components comprises an R/RGB value, a G/RGBvalue, a B/RGB value, an R/G value, an R/B value or a G/B value.
 23. Themethod of claim 22, wherein each color component in the R/RGB value,G/RGB, B/RGB value, R/G value, R/B value and G/B value represents anaverage color value.